A FAS Channel Fitting Strategy Using Extreme Value Distributions for Accurate Outage Performance Evaluation

2026-10-04

AI Summary

This study addresses the limitation of global-accuracy-prioritized strategies in channel fitting for fluid antenna systems, which yield inaccurate left-tail characterization and compromise outage probability evaluation. To overcome this, an outage-probability-oriented channel fitting method is proposed. Specifically, it employs a composite extreme value distribution combining the Generalized Pareto and Generalized Extreme Value distributions to model low-probability tail behavior. A modified mean squared error criterion based on logarithmic-domain errors is designed, and uniform discretization sampling of the cumulative distribution function is introduced to mitigate the underrepresentation caused by sparse tail samples. Simulation results demonstrate that the proposed strategy significantly improves evaluation accuracy in the ultra-low outage probability regime, effectively overcoming the tail-fitting limitations inherent in conventional approaches.

Abstract

Modeling the channel in a single-antenna fluid antenna system (FAS) using extreme value distributions (EVDs) provides an accurate and tractable framework for FAS performance evaluation. When the objective of FAS channel fitting is outage probability (OP) evaluation, accurate characterization of the low-probability left-tail region becomes crucial, while existing fitting strategies that emphasize global fitting accuracy may fail to capture the critical tail behavior required for precise OP evaluation. In this paper, we propose an OP-oriented channel fitting strategy with a left-tail-sensitive target distribution and fitting criterion. Specifically, a combined EVD (CEVD) is introduced as the target distribution, where a generalized Pareto distribution (GPD) is employed to characterize the left tail and a generalized extreme value (GEV) distribution is used to model the global behavior. Furthermore, a modified mean-square-error (MMSE) criterion is developed, which employs logarithmic-domain errors to enhance sensitivity to left-tail discrepancies. Meanwhile, the evaluation points are constructed via uniform discretization on the logarithm of the cumulative distribution function, ensuring uniform sampling across all probability scales. This mitigates the under-representation of tail errors in the overall MMSE, which cannot be effectively addressed by error amplification alone due to the sparsity of tail samples. Simulation results demonstrate that the proposed fitting strategy significantly improves the OP evaluation accuracy in the ultra-low-OP regime.

Problem

• Fluid Antenna System
• Channel Fitting
• Outage Probability
• Extreme Value Distributions
• Left-tail Characterization

Innovation

• Fluid Antenna System
• Extreme Value Distributions
• Outage Probability
• Channel Fitting
• Generalized Pareto Distribution